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<a href="_multi_chain_approximator_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * (at your option) any later version.</span></div>
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<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#ifndef SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_MULTICHAINAPPROXIMATOR_H</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#define SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_MULTICHAINAPPROXIMATOR_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_objective_function_8h.html" title="AbstractObjectiveFunction.">shark/ObjectiveFunctions/AbstractObjectiveFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &quot;Impl/DataEvaluator.h&quot;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{<span class="comment"></span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">///\brief Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel.</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">///</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">///The advantage is, that every chain can produce samples of a different mode of the distribution.</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">///The disadvantage is however, that mixing is slower and a higher value of sampling steps between subsequent samples</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">///need to be chosen. </span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> MarkovChainType&gt; </div>
<div class="foldopen" id="foldopen00044" data-start="{" data-end="};">
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html">   44</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_multi_chain_approximator.html" title="Approximates the gradient by taking samples from an ensemble of Markov chains running in parallel.">MultiChainApproximator</a>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>{</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#ab7ff1b4d01f6a2568f15cd9201bd6469">   46</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> MarkovChainType::RBM <a class="code hl_typedef" href="classshark_1_1_multi_chain_approximator.html#ab7ff1b4d01f6a2568f15cd9201bd6469">RBM</a>;</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    </div>
<div class="foldopen" id="foldopen00048" data-start="{" data-end="}">
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#ab7a3850de8247c8d9ba308fd434b6365">   48</a></span>    <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#ab7a3850de8247c8d9ba308fd434b6365">MultiChainApproximator</a>(<a class="code hl_typedef" href="classshark_1_1_multi_chain_approximator.html#ab7ff1b4d01f6a2568f15cd9201bd6469">RBM</a>* rbm)</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    : mpe_rbm(rbm),m_chainOperator(rbm),m_k(1),m_samples(0),m_numBatches(0),m_regularizer(0){</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(rbm != NULL);</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#aa08ae62e82e9db3257defdcf6bd9fb40">setBatchSize</a>(500);</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span> </div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a>.<a class="code hl_function" href="classshark_1_1_typed_flags.html#a68f0c572adf112b680ef11531aa9ffb8">reset</a>(<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aad3475b458576c8760f28d8d81f4eda86" title="The function can be evaluated and evalDerivative returns a meaningless value (for example std::numeri...">HAS_VALUE</a>);</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |=<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aa0bc7673a369df5f86ddd6ba6735f4971" title="The method evalDerivative is implemented for the first derivative and returns a sensible value.">HAS_FIRST_DERIVATIVE</a>;</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |=<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aab9262b57bb302f04b2561666a9068446" title="The function can propose a sensible starting point to search algorithms.">CAN_PROPOSE_STARTING_POINT</a>;</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    }</div>
</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment"></span> </div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00059" data-start="{" data-end="}">
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#acc43cc0a740984cfeaffbcf565eeb83f">   59</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#acc43cc0a740984cfeaffbcf565eeb83f" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;MultiChainApproximator&quot;</span>; }</div>
</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    </div>
<div class="foldopen" id="foldopen00062" data-start="{" data-end="}">
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a0b325f5f54fb749418f5bc6d18943f4a">   62</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a0b325f5f54fb749418f5bc6d18943f4a">setK</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k){</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>        m_k = k;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    }</div>
</div>
<div class="foldopen" id="foldopen00065" data-start="{" data-end="}">
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a8647286447d32e7f2e7fc4c588b92539">   65</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a8647286447d32e7f2e7fc4c588b92539">setNumberOfSamples</a>(std::size_t samples){</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        m_samples = samples;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    }</div>
</div>
<div class="foldopen" id="foldopen00068" data-start="{" data-end="}">
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#aa08ae62e82e9db3257defdcf6bd9fb40">   68</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#aa08ae62e82e9db3257defdcf6bd9fb40">setBatchSize</a>(std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>){</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        m_batchSize = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        <span class="keywordflow">if</span>(!MarkovChainType::computesBatch)</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>            m_batchSize=1;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    }</div>
</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    </div>
<div class="foldopen" id="foldopen00074" data-start="{" data-end="}">
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a2399386f6eb6a5b3fc39197f1e147440">   74</a></span>    MarkovChainType&amp; <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a2399386f6eb6a5b3fc39197f1e147440">chain</a>(){</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        <span class="keywordflow">return</span> m_chainOperator;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
</div>
<div class="foldopen" id="foldopen00077" data-start="{" data-end="}">
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a06f11a68ce5ad4d07fc217a0b00aecc9">   77</a></span>    MarkovChainType <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a06f11a68ce5ad4d07fc217a0b00aecc9">chain</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="keywordflow">return</span> m_chainOperator;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    }</div>
</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    <span class="comment"></span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// \brief Returns the number of batches of the dataset that are used in every iteration.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">    /// If it is less than all batches, the batches are chosen at random. if it is 0, all batches are used</span></div>
<div class="foldopen" id="foldopen00084" data-start="{" data-end="}">
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a6b774fffc883d7851dd106e4997d2127">   84</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a6b774fffc883d7851dd106e4997d2127" title="Returns the number of batches of the dataset that are used in every iteration.">numBatches</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="keywordflow">return</span> m_numBatches;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    }</div>
</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>    <span class="comment"></span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span><span class="comment">    /// \brief Returns a reference to the number of batches of the dataset that are used in every iteration.</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    /// If it is less than all batches, the batches are chosen at random.if it is 0, all batches are used.</span></div>
<div class="foldopen" id="foldopen00091" data-start="{" data-end="}">
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#ad920b5c96a076ec37f6adc755a4eb4f7">   91</a></span><span class="comment"></span>    std::size_t&amp; <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#ad920b5c96a076ec37f6adc755a4eb4f7" title="Returns a reference to the number of batches of the dataset that are used in every iteration.">numBatches</a>(){</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        <span class="keywordflow">return</span> m_numBatches;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    }</div>
</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    </div>
<div class="foldopen" id="foldopen00095" data-start="{" data-end="}">
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#aefcaae99947142a6dc5bc810b808ba8c">   95</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#aefcaae99947142a6dc5bc810b808ba8c">setData</a>(<a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; data){</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>        m_data = data;</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>        <span class="comment">//construct a gradient object to get the information about which values of the samples are needed</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> grad(mpe_rbm);</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        </div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="comment">//if the number of samples is 0 = unset, set it to the number of points in the data set</span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <span class="keywordflow">if</span>(!m_samples){</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>            <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a8647286447d32e7f2e7fc4c588b92539">setNumberOfSamples</a>(m_data.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>());</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        }</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        </div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="comment">//calculate the number of batches</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        std::size_t batches = m_samples / m_batchSize; </div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">if</span>(m_samples - batches*m_batchSize != 0){</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            ++batches;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        }</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        m_chains.resize(batches);</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        </div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        <span class="comment">//swap every sample batch from the vector into the operator, initialize it and shift it back out.</span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != batches;++i){</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            <a class="code hl_function" href="namespaceshark.html#a3fffe112e8e09ea8f41e4fb7113e93ee" title="Swaps the contents of two instances of KeyValuePair.">swap</a>(m_chains[i],m_chainOperator.samples());</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            std::size_t currentBatchSize = std::min(m_samples-i*m_batchSize, m_batchSize);</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            m_chainOperator.setBatchSize(currentBatchSize);</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            m_chainOperator.initializeChain(m_data);</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            <a class="code hl_function" href="namespaceshark.html#a3fffe112e8e09ea8f41e4fb7113e93ee" title="Swaps the contents of two instances of KeyValuePair.">swap</a>(m_chains[i],m_chainOperator.samples());</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        }</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>    }</div>
</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>    </div>
<div class="foldopen" id="foldopen00123" data-start="{" data-end="}">
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a2ff87fa005fe8314dca5a49c8675adf5">  123</a></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a2ff87fa005fe8314dca5a49c8675adf5" title="Proposes a starting point in the feasible search space of the function.">proposeStartingPoint</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <span class="keywordflow">return</span>  mpe_rbm-&gt;parameterVector();</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    }</div>
</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    </div>
<div class="foldopen" id="foldopen00127" data-start="{" data-end="}">
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a993446d642fcad8eb01773477820547a">  127</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a993446d642fcad8eb01773477820547a" title="Accesses the number of variables.">numberOfVariables</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">return</span> mpe_rbm-&gt;numberOfParameters();</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>    }</div>
</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>    </div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#a9c9cf392354f6f6c785dc90f46d8b1b9">  131</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#a9c9cf392354f6f6c785dc90f46d8b1b9">setRegularizer</a>(<span class="keywordtype">double</span> factor, <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* regularizer){</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        m_regularizer = regularizer;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        m_regularizationStrength = factor;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>    }</div>
</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    </div>
<div class="foldopen" id="foldopen00136" data-start="{" data-end="}">
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_chain_approximator.html#acc8b9a5b1df621311dfc54ef5a70e2e4">  136</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_multi_chain_approximator.html#acc8b9a5b1df621311dfc54ef5a70e2e4" title="Evaluates the objective function and calculates its gradient.">evalDerivative</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp; parameter, <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> &amp; derivative )<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        mpe_rbm-&gt;setParameterVector(parameter);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        </div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> modelAverage(mpe_rbm);</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        RealVector empiricalAverage = detail::evaluateData(m_data,*mpe_rbm,m_numBatches);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="comment">//approximate the expectation of the energy gradient with respect to the model distribution</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="comment">//using samples from the Markov chain</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != m_chains.size();++i){</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <a class="code hl_function" href="namespaceshark.html#a3fffe112e8e09ea8f41e4fb7113e93ee" title="Swaps the contents of two instances of KeyValuePair.">swap</a>(m_chains[i],m_chainOperator.samples());<span class="comment">//set the current GibbsChain</span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            m_chainOperator.step(m_k);<span class="comment">//do the next step along the gibbs chain</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            modelAverage.addVH(m_chainOperator.samples().hidden, m_chainOperator.samples().visible);<span class="comment">//update gradient</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            <a class="code hl_function" href="namespaceshark.html#a3fffe112e8e09ea8f41e4fb7113e93ee" title="Swaps the contents of two instances of KeyValuePair.">swap</a>(m_chains[i],m_chainOperator.samples());<span class="comment">//save the GibbsChain.</span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        }</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        </div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        derivative.resize(mpe_rbm-&gt;numberOfParameters());</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        noalias(derivative) = modelAverage.result() - empiricalAverage;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <span class="keywordflow">if</span>(m_regularizer){</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> regularizerDerivative;</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>            m_regularizer-&gt;<a class="code hl_function" href="classshark_1_1_abstract_objective_function.html#a53df2ac5d82c608ea938dc1e3a0c0617" title="Evaluates the objective function and calculates its gradient.">evalDerivative</a>(parameter,regularizerDerivative);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>            noalias(derivative) += m_regularizationStrength*regularizerDerivative;</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        }</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        </div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <span class="keywordflow">return</span> std::numeric_limits&lt;double&gt;::quiet_NaN();</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>    }</div>
</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    <a class="code hl_class" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a>* mpe_rbm;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    <span class="keyword">mutable</span> MarkovChainType m_chainOperator;</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>    <span class="keyword">mutable</span> std::vector&lt;typename MarkovChainType::SampleBatch&gt; m_chains;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    <a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> m_data;</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_k;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    std::size_t m_samples;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    std::size_t m_batchSize;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    std::size_t m_numBatches;</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* m_regularizer;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    <span class="keywordtype">double</span> m_regularizationStrength;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>};  </div>
</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>}</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span> </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span> </div>
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